Bootstrap Techniques for Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative Gibbsian technique for reconstruction of m-ary images
Pattern Recognition
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NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Multisensor integration and fusion for intelligent machines and systems
Multisensor integration and fusion for intelligent machines and systems
Evaluation and comparison of different segmentation algorithms
Pattern Recognition Letters
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Proceedings of the 1998 conference on Advances in neural information processing systems II
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Pattern Recognition Letters
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ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Bootstrap Coverage Plots for Image Segmentation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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IEEE Transactions on Information Theory
Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation
IEEE Transactions on Image Processing
Pixel-level image fusion with simultaneous orthogonal matching pursuit
Information Fusion
Image matting for fusion of multi-focus images in dynamic scenes
Information Fusion
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Image fusion refers to the techniques that integrate complementary information from multiple image sensors' data in a way that makes the new images more suitable for human visual perception and reduces computation processing tasks. In this paper, we propose a non-parametric and region-based image fusion based on the Bootstrap sampling (BS) principle, which reduces the dependence effect of pixels in real images and minimizes the fusion time. Given an original image, we randomly select a small representative set of pixels. In the statistical image formation model, image sensors are described as the true scene corrupted by additive non-Gaussian distortion. Then, a Non-parametric Expectation-Maximization (NEM) algorithm would be used to estimate both the model parameters and the fused image. The non-parametric aspect comes from the use of the orthogonal series' estimator. Obtained results show that the BS method gives better results than the classical one, both for fused image quality as well as for computation time.